Overview

Dataset statistics

Number of variables22
Number of observations24
Missing cells8
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory184.0 B

Variable types

Numeric15
Categorical7

Alerts

Measured Composition (%).Al has constant value ""Constant
Measured Composition (%).Cu has constant value ""Constant
XRD.Phase has constant value ""Constant
Elongation (Average) has constant value ""Constant
Target Composition (%).Co is highly overall correlated with Target Composition (%).Fe and 9 other fieldsHigh correlation
Target Composition (%).Fe is highly overall correlated with Target Composition (%).Co and 4 other fieldsHigh correlation
Target Composition (%).Ni is highly overall correlated with Target Composition (%).Co and 4 other fieldsHigh correlation
Measured Composition (%).Co is highly overall correlated with Target Composition (%).Co and 9 other fieldsHigh correlation
Measured Composition (%).Cr is highly overall correlated with Target Composition (%).Cr and 1 other fieldsHigh correlation
Measured Composition (%).Fe is highly overall correlated with Target Composition (%).Co and 4 other fieldsHigh correlation
Measured Composition (%).Mn is highly overall correlated with Target Composition (%).Mn and 1 other fieldsHigh correlation
Measured Composition (%).Ni is highly overall correlated with Target Composition (%).Co and 7 other fieldsHigh correlation
Measured Composition (%).V is highly overall correlated with Yield Strength (Average) and 2 other fieldsHigh correlation
XRD.Lattice Parameters is highly overall correlated with Target Composition (%).Co and 4 other fieldsHigh correlation
Elastic Modulus (Average) is highly overall correlated with Target Composition (%).Co and 6 other fieldsHigh correlation
Maximum ∂2σ/∂ε2 (Average) is highly overall correlated with UTS/YS Ratio (Average) and 1 other fieldsHigh correlation
UTS/YS Ratio (Average) is highly overall correlated with Maximum ∂2σ/∂ε2 (Average) and 1 other fieldsHigh correlation
Ultimate Tensile Strength (Average) is highly overall correlated with Target Composition (%).Co and 3 other fieldsHigh correlation
Yield Strength (Average) is highly overall correlated with Measured Composition (%).V and 2 other fieldsHigh correlation
Target Composition (%).Cr is highly overall correlated with Target Composition (%).Co and 5 other fieldsHigh correlation
Target Composition (%).Mn is highly overall correlated with Measured Composition (%).MnHigh correlation
Target Composition (%).V is highly overall correlated with Target Composition (%).Co and 7 other fieldsHigh correlation
XRD.Phase has 1 (4.2%) missing valuesMissing
XRD.Lattice Parameters has 1 (4.2%) missing valuesMissing
Elastic Modulus (Average) has 1 (4.2%) missing valuesMissing
Elongation (Average) has 1 (4.2%) missing valuesMissing
Maximum ∂2σ/∂ε2 (Average) has 1 (4.2%) missing valuesMissing
UTS/YS Ratio (Average) has 1 (4.2%) missing valuesMissing
Ultimate Tensile Strength (Average) has 1 (4.2%) missing valuesMissing
Yield Strength (Average) has 1 (4.2%) missing valuesMissing
Measured Composition (%).Co has unique valuesUnique
Measured Composition (%).Cr has unique valuesUnique
Measured Composition (%).Fe has unique valuesUnique
Measured Composition (%).Mn has unique valuesUnique
Measured Composition (%).Ni has unique valuesUnique
Measured Composition (%).Co has 1 (4.2%) zerosZeros
Measured Composition (%).Cr has 1 (4.2%) zerosZeros
Measured Composition (%).Fe has 1 (4.2%) zerosZeros
Measured Composition (%).Mn has 1 (4.2%) zerosZeros
Measured Composition (%).Ni has 1 (4.2%) zerosZeros
Measured Composition (%).V has 1 (4.2%) zerosZeros

Reproduction

Analysis started2025-04-24 18:45:05.503406
Analysis finished2025-04-24 18:45:42.290006
Duration36.79 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

Target Composition (%).Co
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.166667
Minimum8
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:42.409062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q111
median16
Q328
95-th percentile36
Maximum44
Range36
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.90539
Coefficient of variation (CV)0.54076315
Kurtosis-0.7169036
Mean20.166667
Median Absolute Deviation (MAD)8
Skewness0.58426677
Sum484
Variance118.92754
MonotonicityNot monotonic
2025-04-24T18:45:42.590621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 6
25.0%
16 5
20.8%
36 3
12.5%
24 3
12.5%
28 2
 
8.3%
12 2
 
8.3%
32 1
 
4.2%
20 1
 
4.2%
44 1
 
4.2%
ValueCountFrequency (%)
8 6
25.0%
12 2
 
8.3%
16 5
20.8%
20 1
 
4.2%
24 3
12.5%
28 2
 
8.3%
32 1
 
4.2%
36 3
12.5%
44 1
 
4.2%
ValueCountFrequency (%)
44 1
 
4.2%
36 3
12.5%
32 1
 
4.2%
28 2
 
8.3%
24 3
12.5%
20 1
 
4.2%
16 5
20.8%
12 2
 
8.3%
8 6
25.0%

Target Composition (%).Cr
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size384.0 B
8
17 
12
4

Length

Max length2
Median length1
Mean length1.2083333
Min length1

Characters and Unicode

Total characters29
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 17
70.8%
12 5
 
20.8%
4 2
 
8.3%

Length

2025-04-24T18:45:42.808971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:42.980757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
8 17
70.8%
12 5
 
20.8%
4 2
 
8.3%

Most occurring characters

ValueCountFrequency (%)
8 17
58.6%
1 5
 
17.2%
2 5
 
17.2%
4 2
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 17
58.6%
1 5
 
17.2%
2 5
 
17.2%
4 2
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 29
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 17
58.6%
1 5
 
17.2%
2 5
 
17.2%
4 2
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 17
58.6%
1 5
 
17.2%
2 5
 
17.2%
4 2
 
6.9%

Target Composition (%).Fe
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum8
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:43.150563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q18
median12
Q320
95-th percentile32
Maximum32
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.607378
Coefficient of variation (CV)0.5738252
Kurtosis-0.23211913
Mean15
Median Absolute Deviation (MAD)4
Skewness1.0674819
Sum360
Variance74.086957
MonotonicityNot monotonic
2025-04-24T18:45:43.336727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 10
41.7%
12 5
20.8%
32 3
 
12.5%
20 2
 
8.3%
16 2
 
8.3%
24 1
 
4.2%
28 1
 
4.2%
ValueCountFrequency (%)
8 10
41.7%
12 5
20.8%
16 2
 
8.3%
20 2
 
8.3%
24 1
 
4.2%
28 1
 
4.2%
32 3
 
12.5%
ValueCountFrequency (%)
32 3
 
12.5%
28 1
 
4.2%
24 1
 
4.2%
20 2
 
8.3%
16 2
 
8.3%
12 5
20.8%
8 10
41.7%

Target Composition (%).Mn
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size384.0 B
8
13 
12
11 

Length

Max length2
Median length1
Mean length1.4583333
Min length1

Characters and Unicode

Total characters35
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row12
3rd row12
4th row12
5th row8

Common Values

ValueCountFrequency (%)
8 13
54.2%
12 11
45.8%

Length

2025-04-24T18:45:43.536550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:43.691003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
8 13
54.2%
12 11
45.8%

Most occurring characters

ValueCountFrequency (%)
8 13
37.1%
1 11
31.4%
2 11
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 13
37.1%
1 11
31.4%
2 11
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 35
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 13
37.1%
1 11
31.4%
2 11
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 13
37.1%
1 11
31.4%
2 11
31.4%

Target Composition (%).Ni
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.833333
Minimum20
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:43.828099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28
Q136
median40
Q341
95-th percentile44
Maximum48
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.5651694
Coefficient of variation (CV)0.17352871
Kurtosis1.0787472
Mean37.833333
Median Absolute Deviation (MAD)4
Skewness-1.0781618
Sum908
Variance43.101449
MonotonicityNot monotonic
2025-04-24T18:45:43.988969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 9
37.5%
44 5
20.8%
36 4
16.7%
28 3
 
12.5%
32 1
 
4.2%
48 1
 
4.2%
20 1
 
4.2%
ValueCountFrequency (%)
20 1
 
4.2%
28 3
 
12.5%
32 1
 
4.2%
36 4
16.7%
40 9
37.5%
44 5
20.8%
48 1
 
4.2%
ValueCountFrequency (%)
48 1
 
4.2%
44 5
20.8%
40 9
37.5%
36 4
16.7%
32 1
 
4.2%
28 3
 
12.5%
20 1
 
4.2%

Target Composition (%).V
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size384.0 B
8
11 
12
4
16
 
1

Length

Max length2
Median length1
Mean length1.3333333
Min length1

Characters and Unicode

Total characters32
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row8
2nd row12
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 11
45.8%
12 7
29.2%
4 5
20.8%
16 1
 
4.2%

Length

2025-04-24T18:45:44.213666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:44.569765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
8 11
45.8%
12 7
29.2%
4 5
20.8%
16 1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
8 11
34.4%
1 8
25.0%
2 7
21.9%
4 5
15.6%
6 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 11
34.4%
1 8
25.0%
2 7
21.9%
4 5
15.6%
6 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 11
34.4%
1 8
25.0%
2 7
21.9%
4 5
15.6%
6 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 11
34.4%
1 8
25.0%
2 7
21.9%
4 5
15.6%
6 1
 
3.1%

Measured Composition (%).Al
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size384.0 B
0
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24
100.0%

Length

2025-04-24T18:45:44.751108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:44.897228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 24
100.0%

Most occurring characters

ValueCountFrequency (%)
0 24
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
100.0%

Measured Composition (%).Co
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.376496
Minimum0
Maximum43.465333
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:45.054491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.1072333
Q18.3005
median16.137286
Q327.929667
95-th percentile35.906133
Maximum43.465333
Range43.465333
Interquartile range (IQR)19.629167

Descriptive statistics

Standard deviation11.499142
Coefficient of variation (CV)0.59345829
Kurtosis-0.7437574
Mean19.376496
Median Absolute Deviation (MAD)7.9082601
Skewness0.44768079
Sum465.03591
Variance132.23027
MonotonicityNot monotonic
2025-04-24T18:45:45.275126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8.163333333 1
 
4.2%
16.17928571 1
 
4.2%
27.92866667 1
 
4.2%
8.294 1
 
4.2%
15.99133333 1
 
4.2%
43.46533333 1
 
4.2%
23.91466667 1
 
4.2%
35.84266667 1
 
4.2%
0 1
 
4.2%
12.146 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
8.097333333 1
4.2%
8.163333333 1
4.2%
8.205384615 1
4.2%
8.252666667 1
4.2%
8.294 1
4.2%
8.302666667 1
4.2%
12.146 1
4.2%
12.26 1
4.2%
15.99133333 1
4.2%
ValueCountFrequency (%)
43.46533333 1
4.2%
35.91733333 1
4.2%
35.84266667 1
4.2%
35.71466667 1
4.2%
31.958 1
4.2%
27.93266667 1
4.2%
27.92866667 1
4.2%
24.22066667 1
4.2%
23.98266667 1
4.2%
23.91466667 1
4.2%

Measured Composition (%).Cr
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.563623
Minimum0
Maximum12.352667
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:45.486663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7453333
Q18.2035
median8.2417857
Q38.2981667
95-th percentile12.343433
Maximum12.352667
Range12.352667
Interquartile range (IQR)0.094666667

Descriptive statistics

Standard deviation2.6769808
Coefficient of variation (CV)0.3125991
Kurtosis3.9722312
Mean8.563623
Median Absolute Deviation (MAD)0.046333333
Skewness-1.1159372
Sum205.52695
Variance7.1662265
MonotonicityNot monotonic
2025-04-24T18:45:45.692620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8.292666667 1
 
4.2%
8.243571429 1
 
4.2%
12.29866667 1
 
4.2%
4.163333333 1
 
4.2%
12.35266667 1
 
4.2%
12.262 1
 
4.2%
12.35133333 1
 
4.2%
8.181333333 1
 
4.2%
0 1
 
4.2%
8.258666667 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
4.163333333 1
4.2%
8.043333333 1
4.2%
8.158666667 1
4.2%
8.181333333 1
4.2%
8.2 1
4.2%
8.204666667 1
4.2%
8.219333333 1
4.2%
8.22 1
4.2%
8.220714286 1
4.2%
ValueCountFrequency (%)
12.35266667 1
4.2%
12.35133333 1
4.2%
12.29866667 1
4.2%
12.276 1
4.2%
12.262 1
4.2%
8.314666667 1
4.2%
8.292666667 1
4.2%
8.277333333 1
4.2%
8.268 1
4.2%
8.258666667 1
4.2%

Measured Composition (%).Cu
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size384.0 B
0
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24
100.0%

Length

2025-04-24T18:45:45.893319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:46.040922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 24
100.0%

Most occurring characters

ValueCountFrequency (%)
0 24
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24
100.0%

Measured Composition (%).Fe
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.757624
Minimum0
Maximum32.111333
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:46.204928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.066
Q18.1513333
median12.098
Q320.100333
95-th percentile31.98
Maximum32.111333
Range32.111333
Interquartile range (IQR)11.949

Descriptive statistics

Standard deviation8.9883365
Coefficient of variation (CV)0.60906394
Kurtosis-0.24412768
Mean14.757624
Median Absolute Deviation (MAD)3.9876667
Skewness0.837731
Sum354.18297
Variance80.790193
MonotonicityNot monotonic
2025-04-24T18:45:46.418743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
24.02466667 1
 
4.2%
8.172142857 1
 
4.2%
8.1 1
 
4.2%
31.994 1
 
4.2%
12.07933333 1
 
4.2%
8.120666667 1
 
4.2%
16.15666667 1
 
4.2%
8.172666667 1
 
4.2%
0 1
 
4.2%
8.144666667 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
8.06 1
4.2%
8.1 1
4.2%
8.120666667 1
4.2%
8.144666667 1
4.2%
8.147333333 1
4.2%
8.152666667 1
4.2%
8.156666667 1
4.2%
8.172142857 1
4.2%
8.172666667 1
4.2%
ValueCountFrequency (%)
32.11133333 1
4.2%
31.994 1
4.2%
31.90066667 1
4.2%
27.86692308 1
4.2%
24.02466667 1
4.2%
20.11733333 1
4.2%
20.09466667 1
4.2%
16.15666667 1
4.2%
16.116 1
4.2%
12.15 1
4.2%

Measured Composition (%).Mn
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5133289
Minimum0
Maximum12.357333
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:46.616609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.1196667
Q18.1643333
median8.2136667
Q312.159667
95-th percentile12.302333
Maximum12.357333
Range12.357333
Interquartile range (IQR)3.9953333

Descriptive statistics

Standard deviation2.8464022
Coefficient of variation (CV)0.29920149
Kurtosis4.1009532
Mean9.5133289
Median Absolute Deviation (MAD)0.082333333
Skewness-1.4327674
Sum228.31989
Variance8.1020053
MonotonicityNot monotonic
2025-04-24T18:45:46.824470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8.163333333 1
 
4.2%
12.10214286 1
 
4.2%
8.19 1
 
4.2%
8.148 1
 
4.2%
8.150666667 1
 
4.2%
12.19266667 1
 
4.2%
8.164666667 1
 
4.2%
12.26266667 1
 
4.2%
0 1
 
4.2%
12.11866667 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
8.114666667 1
4.2%
8.148 1
4.2%
8.150666667 1
4.2%
8.153846154 1
4.2%
8.163333333 1
4.2%
8.164666667 1
4.2%
8.168666667 1
4.2%
8.173333333 1
4.2%
8.18 1
4.2%
ValueCountFrequency (%)
12.35733333 1
4.2%
12.30933333 1
4.2%
12.26266667 1
4.2%
12.242 1
4.2%
12.20857143 1
4.2%
12.19266667 1
4.2%
12.14866667 1
4.2%
12.11866667 1
4.2%
12.10933333 1
4.2%
12.10214286 1
4.2%

Measured Composition (%).Ni
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.45986
Minimum0
Maximum47.146
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:47.023531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.984967
Q134.226667
median39.144524
Q340.306552
95-th percentile43.200833
Maximum47.146
Range47.146
Interquartile range (IQR)6.0798855

Descriptive statistics

Standard deviation9.9006595
Coefficient of variation (CV)0.27920752
Kurtosis6.4981595
Mean35.45986
Median Absolute Deviation (MAD)3.8635238
Skewness-2.235123
Sum851.03664
Variance98.023058
MonotonicityNot monotonic
2025-04-24T18:45:47.244097image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
43.164 1
 
4.2%
42.98928571 1
 
4.2%
35.28066667 1
 
4.2%
39.206 1
 
4.2%
39.258 1
 
4.2%
19.87066667 1
 
4.2%
35.28133333 1
 
4.2%
27.36733333 1
 
4.2%
0 1
 
4.2%
47.146 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
19.87066667 1
4.2%
27.29933333 1
4.2%
27.36733333 1
4.2%
27.70533333 1
4.2%
31.06466667 1
4.2%
35.28066667 1
4.2%
35.28133333 1
4.2%
35.32333333 1
4.2%
35.43733333 1
4.2%
ValueCountFrequency (%)
47.146 1
4.2%
43.20733333 1
4.2%
43.164 1
4.2%
43.15866667 1
4.2%
43.08933333 1
4.2%
42.98928571 1
4.2%
39.41230769 1
4.2%
39.258 1
4.2%
39.21533333 1
4.2%
39.206 1
4.2%

Measured Composition (%).V
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1627163
Minimum0
Maximum12.31
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:47.452073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.0842333
Q17.1376667
median8.1916667
Q312.108964
95-th percentile12.259667
Maximum12.31
Range12.31
Interquartile range (IQR)4.9712976

Descriptive statistics

Standard deviation3.380066
Coefficient of variation (CV)0.41408594
Kurtosis-0.12674881
Mean8.1627163
Median Absolute Deviation (MAD)3.9335952
Skewness-0.51566357
Sum195.90519
Variance11.424846
MonotonicityNot monotonic
2025-04-24T18:45:47.657844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8.198666667 2
 
8.3%
4.121333333 1
 
4.2%
8.195333333 1
 
4.2%
12.168 1
 
4.2%
4.090666667 1
 
4.2%
4.130666667 1
 
4.2%
8.177333333 1
 
4.2%
0 1
 
4.2%
12.186 1
 
4.2%
12.09266667 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0 1
4.2%
4.083333333 1
4.2%
4.089333333 1
4.2%
4.090666667 1
4.2%
4.121333333 1
4.2%
4.130666667 1
4.2%
8.14 1
4.2%
8.166 1
4.2%
8.176666667 1
4.2%
8.177333333 1
4.2%
ValueCountFrequency (%)
12.31 1
4.2%
12.27266667 1
4.2%
12.186 1
4.2%
12.168 1
4.2%
12.16266667 1
4.2%
12.15785714 1
4.2%
12.09266667 1
4.2%
8.216 1
4.2%
8.200666667 1
4.2%
8.198666667 2
8.3%

XRD.Phase
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.3%
Missing1
Missing (%)4.2%
Memory size384.0 B
FCC
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFCC
2nd rowFCC
3rd rowFCC
4th rowFCC
5th rowFCC

Common Values

ValueCountFrequency (%)
FCC 23
95.8%
(Missing) 1
 
4.2%

Length

2025-04-24T18:45:47.854195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:48.002470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
fcc 23
100.0%

Most occurring characters

ValueCountFrequency (%)
C 46
66.7%
F 23
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 69
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 46
66.7%
F 23
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 69
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 46
66.7%
F 23
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 46
66.7%
F 23
33.3%

XRD.Lattice Parameters
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.5844896
Minimum3.5698102
Maximum3.5967216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:48.178655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3.5698102
5-th percentile3.5730697
Q13.5785232
median3.5850582
Q33.5907875
95-th percentile3.5952489
Maximum3.5967216
Range0.0269114
Interquartile range (IQR)0.01226425

Descriptive statistics

Standard deviation0.0080395439
Coefficient of variation (CV)0.0022428699
Kurtosis-1.0567386
Mean3.5844896
Median Absolute Deviation (MAD)0.0062808
Skewness-0.32314266
Sum82.44326
Variance6.4634266 × 10-5
MonotonicityNot monotonic
2025-04-24T18:45:48.418174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3.592027 1
 
4.2%
3.5967216 1
 
4.2%
3.583352 1
 
4.2%
3.591339 1
 
4.2%
3.5871623 1
 
4.2%
3.5698102 1
 
4.2%
3.5758352 1
 
4.2%
3.57356 1
 
4.2%
3.5953739 1
 
4.2%
3.5850582 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
3.5698102 1
4.2%
3.5730152 1
4.2%
3.57356 1
4.2%
3.5736487 1
4.2%
3.5738897 1
4.2%
3.5758352 1
4.2%
3.5812113 1
4.2%
3.5815904 1
4.2%
3.582482 1
4.2%
3.583352 1
4.2%
ValueCountFrequency (%)
3.5967216 1
4.2%
3.5953739 1
4.2%
3.5941243 1
4.2%
3.592766 1
4.2%
3.592027 1
4.2%
3.591339 1
4.2%
3.590236 1
4.2%
3.5902166 1
4.2%
3.5877101 1
4.2%
3.587536 1
4.2%

Elastic Modulus (Average)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean224.01509
Minimum190.83507
Maximum259.14268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:48.618370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum190.83507
5-th percentile196.8504
Q1212.97009
median223.30391
Q3235.84749
95-th percentile246.60965
Maximum259.14268
Range68.307612
Interquartile range (IQR)22.877393

Descriptive statistics

Standard deviation16.837647
Coefficient of variation (CV)0.075163001
Kurtosis-0.25951697
Mean224.01509
Median Absolute Deviation (MAD)11.98845
Skewness0.030427756
Sum5152.3472
Variance283.50635
MonotonicityNot monotonic
2025-04-24T18:45:48.829184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
215.5451104 1
 
4.2%
221.3794206 1
 
4.2%
259.1426819 1
 
4.2%
190.8350702 1
 
4.2%
225.3040484 1
 
4.2%
245.2299385 1
 
4.2%
211.7628001 1
 
4.2%
239.4372429 1
 
4.2%
223.3039135 1
 
4.2%
242.1826928 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
190.8350702 1
4.2%
195.8711955 1
4.2%
205.6632035 1
4.2%
208.6285894 1
4.2%
209.3205343 1
4.2%
211.7628001 1
4.2%
214.1773853 1
4.2%
215.5451104 1
4.2%
216.5645491 1
4.2%
221.1285993 1
4.2%
ValueCountFrequency (%)
259.1426819 1
4.2%
246.762953 1
4.2%
245.2299385 1
4.2%
242.1826928 1
4.2%
239.4372429 1
4.2%
236.4026076 1
4.2%
235.2923632 1
4.2%
233.4486797 1
4.2%
227.6259295 1
4.2%
227.3376607 1
4.2%

Elongation (Average)
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)4.3%
Missing1
Missing (%)4.2%
Memory size384.0 B
0.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 23
95.8%
(Missing) 1
 
4.2%

Length

2025-04-24T18:45:49.052582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-24T18:45:49.210865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 23
100.0%

Most occurring characters

ValueCountFrequency (%)
0 46
66.7%
. 23
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46
66.7%
Other Punctuation 23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46
100.0%
Other Punctuation
ValueCountFrequency (%)
. 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46
66.7%
. 23
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46
66.7%
. 23
33.3%

Maximum ∂2σ/∂ε2 (Average)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean-2133.9189
Minimum-43248.952
Maximum3656.5126
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)41.7%
Memory size384.0 B
2025-04-24T18:45:49.373426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-43248.952
5-th percentile-16269.064
Q1-994.37848
median261.01651
Q31594.2013
95-th percentile3575.9836
Maximum3656.5126
Range46905.465
Interquartile range (IQR)2588.5797

Descriptive statistics

Standard deviation9922.9144
Coefficient of variation (CV)-4.6500897
Kurtosis14.542944
Mean-2133.9189
Median Absolute Deviation (MAD)1266.9205
Skewness-3.67993
Sum-49080.135
Variance98464230
MonotonicityNot monotonic
2025-04-24T18:45:49.578992image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3601.234057 1
 
4.2%
-43248.95243 1
 
4.2%
1120.507149 1
 
4.2%
261.0165111 1
 
4.2%
-26.22698389 1
 
4.2%
706.6345039 1
 
4.2%
3348.72907 1
 
4.2%
-2633.687195 1
 
4.2%
-982.8530149 1
 
4.2%
-1005.903941 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
-43248.95243 1
4.2%
-17687.03614 1
4.2%
-3507.314556 1
4.2%
-2633.687195 1
4.2%
-1922.005075 1
4.2%
-1005.903941 1
4.2%
-982.8530149 1
4.2%
-826.8586324 1
4.2%
-659.1002921 1
4.2%
-26.22698389 1
4.2%
ValueCountFrequency (%)
3656.512584 1
4.2%
3601.234057 1
4.2%
3348.72907 1
4.2%
2776.391124 1
4.2%
2730.328359 1
4.2%
1668.491422 1
4.2%
1519.911103 1
4.2%
1205.505111 1
4.2%
1120.507149 1
4.2%
719.2577783 1
4.2%

UTS/YS Ratio (Average)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.7339825
Minimum1.6438262
Maximum4.6121367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:49.758855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.6438262
5-th percentile2.1084312
Q13.5359917
median3.9328774
Q34.2702731
95-th percentile4.595813
Maximum4.6121367
Range2.9683105
Interquartile range (IQR)0.73428137

Descriptive statistics

Standard deviation0.77837461
Coefficient of variation (CV)0.20845695
Kurtosis1.6134495
Mean3.7339825
Median Absolute Deviation (MAD)0.36745167
Skewness-1.3959023
Sum85.881598
Variance0.60586704
MonotonicityNot monotonic
2025-04-24T18:45:49.958344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4.3003291 1
 
4.2%
2.051179515 1
 
4.2%
3.951936333 1
 
4.2%
3.812527179 1
 
4.2%
3.790421067 1
 
4.2%
4.240217116 1
 
4.2%
4.612136673 1
 
4.2%
2.921949528 1
 
4.2%
2.623696009 1
 
4.2%
3.872171135 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
1.643826197 1
4.2%
2.051179515 1
4.2%
2.623696009 1
4.2%
2.921949528 1
4.2%
3.240430924 1
4.2%
3.496943406 1
4.2%
3.575040073 1
4.2%
3.790421067 1
4.2%
3.812527179 1
4.2%
3.830501324 1
4.2%
ValueCountFrequency (%)
4.612136673 1
4.2%
4.606211519 1
4.2%
4.502226417 1
4.2%
4.395187031 1
4.2%
4.31559667 1
4.2%
4.3003291 1
4.2%
4.240217116 1
4.2%
4.090320868 1
4.2%
4.045374931 1
4.2%
4.030497734 1
4.2%

Ultimate Tensile Strength (Average)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean862.13712
Minimum617.96629
Maximum1057.3236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:50.183489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum617.96629
5-th percentile697.72971
Q1768.17432
median830.96369
Q3982.11003
95-th percentile1054.0351
Maximum1057.3236
Range439.35732
Interquartile range (IQR)213.93572

Descriptive statistics

Standard deviation130.93456
Coefficient of variation (CV)0.15187208
Kurtosis-1.001972
Mean862.13712
Median Absolute Deviation (MAD)82.566648
Skewness0.13629993
Sum19829.154
Variance17143.859
MonotonicityNot monotonic
2025-04-24T18:45:50.405364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
755.7763289 1
 
4.2%
696.5249894 1
 
4.2%
895.6656926 1
 
4.2%
780.5723014 1
 
4.2%
1054.173142 1
 
4.2%
864.8776567 1
 
4.2%
791.0362753 1
 
4.2%
823.900857 1
 
4.2%
721.0611784 1
 
4.2%
1052.793103 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
617.9662862 1
4.2%
696.5249894 1
4.2%
708.5721574 1
4.2%
721.0611784 1
4.2%
748.3970437 1
4.2%
755.7763289 1
4.2%
780.5723014 1
4.2%
791.0362753 1
4.2%
800.9524422 1
4.2%
818.9677998 1
4.2%
ValueCountFrequency (%)
1057.323601 1
4.2%
1054.173142 1
4.2%
1052.793103 1
4.2%
1029.209881 1
4.2%
1027.431771 1
4.2%
1027.114459 1
4.2%
937.1056026 1
4.2%
897.9108657 1
4.2%
895.6656926 1
4.2%
890.8566537 1
4.2%

Yield Strength (Average)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean240.26717
Minimum161.2184
Maximum375.53677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2025-04-24T18:45:50.606047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum161.2184
5-th percentile172.04578
Q1196.86517
median245.0309
Q3273.68432
95-th percentile332.05891
Maximum375.53677
Range214.31836
Interquartile range (IQR)76.81914

Descriptive statistics

Standard deviation54.495521
Coefficient of variation (CV)0.22681218
Kurtosis0.2477374
Mean240.26717
Median Absolute Deviation (MAD)40.081458
Skewness0.61897018
Sum5526.145
Variance2969.7618
MonotonicityNot monotonic
2025-04-24T18:45:50.791322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
175.7510535 1
 
4.2%
336.2854607 1
 
4.2%
226.0697548 1
 
4.2%
204.9494414 1
 
4.2%
278.1384051 1
 
4.2%
204.2331112 1
 
4.2%
171.6340795 1
 
4.2%
280.848778 1
 
4.2%
275.2505391 1
 
4.2%
272.118091 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
161.2184029 1
4.2%
171.6340795 1
4.2%
175.7510535 1
4.2%
180.4251687 1
4.2%
185.1793879 1
4.2%
195.83826 1
4.2%
197.8920893 1
4.2%
204.2331112 1
4.2%
204.9494414 1
4.2%
226.0697548 1
4.2%
ValueCountFrequency (%)
375.5367675 1
4.2%
336.2854607 1
4.2%
294.0199612 1
4.2%
280.848778 1
4.2%
278.1384051 1
4.2%
275.2505391 1
4.2%
272.118091 1
4.2%
262.1828433 1
4.2%
261.2720432 1
4.2%
255.3718761 1
4.2%

Interactions

2025-04-24T18:45:38.905701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:06.741044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.109337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.348934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.604618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.171993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.416697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.626987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:22.901782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:25.066933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.472265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.655293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.047963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.286151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.744820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.059632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:06.912550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.290370image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.509362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.767578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.337506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.578870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.782873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.057989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:25.241162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.622489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.820446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.224180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.446871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.896337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.203465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.064714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.424720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.654580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.907308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.474356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.714618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.916338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.202469image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:25.600672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.754856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.965178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.365946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.587798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.035851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.348989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.233778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.561618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.800703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.059358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.622476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.867128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.064764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.349602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:25.739960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.898626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.132918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.505938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.733166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.192771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.492529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.387231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.695793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.936608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.203998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.768192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.001209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.217032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.475505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:25.878876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.058037image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.320204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.659248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.896257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.362221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.616164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.541747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.843012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:12.102401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.350947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.917122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.171804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.371721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.614022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.024153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.210611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.473922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.795783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:35.275119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.496613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.741754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.696204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:09.988708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:12.269348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.494484image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:17.077528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.332297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.515584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.754761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.190945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.361337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.627283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:32.940215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:35.415814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.626812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:39.868939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.851518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:10.153476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:12.419352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.636325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:17.246749image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.482898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.673650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:23.905545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.350038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.495970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.771394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.093000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:35.555942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.757814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.000216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:07.993348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:10.293843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:12.546296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:14.771954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:17.392849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.621249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.828049image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:24.037459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.481524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.634945image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:30.925509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.255494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:35.708725image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:37.902543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.146350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-24T18:45:10.438690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-24T18:45:17.544222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.770557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:21.984006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:24.198757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.621718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.768148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.080237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.390155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:35.853362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.041944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.297495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:08.341476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:10.583090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:12.853698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:15.087247image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:17.682780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:19.907525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-24T18:45:24.353021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.759305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:28.909398image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.262240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.536820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.004075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.196742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.449651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:08.506106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:10.753224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.014724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-24T18:45:17.835942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.065248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-04-24T18:45:24.508710image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:26.915092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.072351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.439140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.692783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.188177image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.368260image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.585245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:08.650365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:10.887633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.168589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:15.410323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:17.973941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.212959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:22.459844image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:24.644682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.057654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.224832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.593777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.831809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.330746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.505802image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.729477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:08.811698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.042727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.328621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:15.568618image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.135388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.359023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:22.614976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:24.791907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.213651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.382039image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.751078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:33.982226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.472699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.650744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:40.858639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:08.955207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:11.213840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:13.470357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:16.017949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:18.285734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:20.496163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:22.763355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:24.930690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:27.351760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:29.523691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:31.902502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:34.136416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:36.615108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-24T18:45:38.777699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-04-24T18:45:50.955950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Target Composition (%).CoTarget Composition (%).FeTarget Composition (%).NiMeasured Composition (%).CoMeasured Composition (%).CrMeasured Composition (%).FeMeasured Composition (%).MnMeasured Composition (%).NiMeasured Composition (%).VXRD.Lattice ParametersElastic Modulus (Average)Maximum ∂2σ/∂ε2 (Average)UTS/YS Ratio (Average)Ultimate Tensile Strength (Average)Yield Strength (Average)Target Composition (%).CrTarget Composition (%).MnTarget Composition (%).V
Target Composition (%).Co1.000-0.797-0.8090.9050.217-0.7240.328-0.791-0.021-0.7530.773-0.1420.1360.5450.1820.5010.4870.584
Target Composition (%).Fe-0.7971.0000.417-0.656-0.1060.958-0.3270.516-0.3030.405-0.8030.2840.121-0.416-0.3910.0000.4400.000
Target Composition (%).Ni-0.8090.4171.000-0.765-0.2200.345-0.2350.8930.2870.736-0.576-0.015-0.278-0.3670.0260.3400.4600.000
Measured Composition (%).Co0.905-0.656-0.7651.0000.316-0.5260.427-0.6210.123-0.7410.738-0.2170.0740.5190.2540.5010.4870.584
Measured Composition (%).Cr0.217-0.106-0.2200.3161.000-0.1050.010-0.0810.095-0.1600.1010.1440.2180.059-0.0960.9760.1600.584
Measured Composition (%).Fe-0.7240.9580.345-0.526-0.1051.000-0.1550.486-0.2690.413-0.8190.1750.040-0.484-0.3030.3110.3950.556
Measured Composition (%).Mn0.328-0.327-0.2350.4270.010-0.1551.000-0.1240.1640.0160.271-0.256-0.180-0.0550.1590.4660.9770.667
Measured Composition (%).Ni-0.7910.5160.893-0.621-0.0810.486-0.1241.0000.3600.692-0.5820.103-0.171-0.339-0.0840.5650.4960.557
Measured Composition (%).V-0.021-0.3030.2870.1230.095-0.2690.1640.3601.0000.4080.290-0.429-0.4790.3120.5240.5100.0001.000
XRD.Lattice Parameters-0.7530.4050.736-0.741-0.1600.4130.0160.6920.4081.000-0.458-0.236-0.444-0.5580.1170.1230.3760.480
Elastic Modulus (Average)0.773-0.803-0.5760.7380.101-0.8190.271-0.5820.290-0.4581.000-0.253-0.0450.5600.2860.2770.4150.151
Maximum ∂2σ/∂ε2 (Average)-0.1420.284-0.015-0.2170.1440.175-0.2560.103-0.429-0.236-0.2531.0000.904-0.023-0.8860.0000.4040.115
UTS/YS Ratio (Average)0.1360.121-0.2780.0740.2180.040-0.180-0.171-0.479-0.444-0.0450.9041.0000.144-0.8720.0000.2210.288
Ultimate Tensile Strength (Average)0.545-0.416-0.3670.5190.059-0.484-0.055-0.3390.312-0.5580.560-0.0230.1441.0000.1960.0000.0000.267
Yield Strength (Average)0.182-0.3910.0260.254-0.096-0.3030.159-0.0840.5240.1170.286-0.886-0.8720.1961.0000.1610.3550.435
Target Composition (%).Cr0.5010.0000.3400.5010.9760.3110.4660.5650.5100.1230.2770.0000.0000.0000.1611.0000.0000.510
Target Composition (%).Mn0.4870.4400.4600.4870.1600.3950.9770.4960.0000.3760.4150.4040.2210.0000.3550.0001.0000.000
Target Composition (%).V0.5840.0000.0000.5840.5840.5560.6670.5571.0000.4800.1510.1150.2880.2670.4350.5100.0001.000

Missing values

2025-04-24T18:45:41.088659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-24T18:45:41.655613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-24T18:45:42.060148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Target Composition (%).CoTarget Composition (%).CrTarget Composition (%).FeTarget Composition (%).MnTarget Composition (%).NiTarget Composition (%).VMeasured Composition (%).AlMeasured Composition (%).CoMeasured Composition (%).CrMeasured Composition (%).CuMeasured Composition (%).FeMeasured Composition (%).MnMeasured Composition (%).NiMeasured Composition (%).VXRD.PhaseXRD.Lattice ParametersElastic Modulus (Average)Elongation (Average)Maximum ∂2σ/∂ε2 (Average)UTS/YS Ratio (Average)Ultimate Tensile Strength (Average)Yield Strength (Average)
CBA01_VAM-A8824844808.1633338.292667024.0246678.16333343.1640008.198667FCC3.592027215.5451100.03601.2340574.300329755.776329175.751054
CBA02_VAM-A1688124412016.1792868.24357108.17214312.10214342.98928612.310000FCC3.596722221.3794210.0-43248.9524282.051180696.524989336.285461
CBA03_VAM-A328812328031.9580008.31466708.15266712.30933331.0646678.200667FCC3.581590236.4026080.0-659.1002923.9328771027.431771261.272043
CBA04_VAM-A88201244808.0973338.043333020.11733312.35733343.2073338.176667FCC3.592766205.6632040.02730.3283594.045375748.397044185.179388
CBA05_VAM-A168168448016.1160008.219333016.1160008.19600043.1586678.188000FCC3.581211209.3205340.0105.2847263.575040937.105603262.182843
CBA06_VAM-A8832840408.3026678.258000032.1113338.16866739.0693334.089333FCC3.590217208.6285890.0-17687.0361451.643826617.966286375.536768
CBA07_VAM-A16812124012015.9920008.222000012.08666712.24200039.18333312.272667FCC3.594124227.6259290.0-3507.3145563.240431818.967800252.443828
CBA08_VAM-A16812124012016.1585718.220714012.14857112.20857139.10571412.157857FCC3.587536221.1285990.0719.2577784.0304981029.209881255.371876
CBA09_VAM-A2812128364027.93266712.276000012.1500008.23400035.3233334.083333FCC3.573890214.1773850.01205.5051114.606212830.963691180.425169
CBA10_VAM-A128208448012.2600008.200000020.0946678.17333343.0893338.182667FCC3.587710227.3376610.0-826.8586323.830501897.910866234.454769
Target Composition (%).CoTarget Composition (%).CrTarget Composition (%).FeTarget Composition (%).MnTarget Composition (%).NiTarget Composition (%).VMeasured Composition (%).AlMeasured Composition (%).CoMeasured Composition (%).CrMeasured Composition (%).CuMeasured Composition (%).FeMeasured Composition (%).MnMeasured Composition (%).NiMeasured Composition (%).VXRD.PhaseXRD.Lattice ParametersElastic Modulus (Average)Elongation (Average)Maximum ∂2σ/∂ε2 (Average)UTS/YS Ratio (Average)Ultimate Tensile Strength (Average)Yield Strength (Average)
CBA15_VAM-A2481283612023.9826678.204667012.1093338.18000035.43733312.092667FCC3.585058242.1826930.0-1005.9039413.8721711052.793103272.118091
CBA16_VAM-A1288124812012.1460008.25866708.14466712.11866747.14600012.186000FCC3.595374223.3039130.0-982.8530152.623696721.061178275.250539
CBA17_VAM-A204812401600.0000000.00000000.0000000.0000000.0000000.000000NoneNaNNaNNaNNaNNaNNaNNaN
CBA18_VAM-A368812288035.8426678.18133308.17266712.26266727.3673338.177333FCC3.573560239.4372430.0-2633.6871952.921950823.900857280.848778
CBA19_VAM-A2412168364023.91466712.351333016.1566678.16466735.2813334.130667FCC3.575835211.7628000.03348.7290704.612137791.036275171.634079
CBA20_VAM-A4412812204043.46533312.26200008.12066712.19266719.8706674.090667FCC3.569810245.2299390.0706.6345044.240217864.877657204.233111
CBA21_VAM-A16121284012015.99133312.352667012.0793338.15066739.25800012.168000FCC3.587162225.3040480.0-26.2269843.7904211054.173142278.138405
CBA22_VAM-A8432840808.2940004.163333031.9940008.14800039.2060008.195333FCC3.591339190.8350700.0261.0165113.812527780.572301204.949441
CBA23_VAM-A281288368027.92866712.29866708.1000008.19000035.2806678.198667FCC3.583352259.1426820.01120.5071493.951936895.665693226.069755
CBA24_VAM-A368812288035.9173338.26800008.15666712.14866727.2993338.216000FCC3.582482235.2923630.0-1922.0050753.4969431027.114459294.019961